Academic Performance and College Dropout: Using Longitudinal Expectations Data to Estimate a Learning Model
Academic Performance and College Dropout: Using Longitudinal Expectations Data to Estimate a Learning Model
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学业表现和大学辍学：使用纵向期望数据来估计学习模型
DOI：
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发表时间：
2013
影响因子：
3.8
通讯作者：
Todd R. Stinebrickner
中科院分区：
文献类型：
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作者：
Ralph Stinebrickner;Todd R. Stinebrickner
我们估计了一个大学辍学的动态学习模型，利用独特的期望数据，大大减少了我们对标准假设的依赖。我们的模拟显示，大学前两年45%的辍学率可以归因于学生对自己学习成绩的了解，而这种学习在大学后期的作用较小。成绩差的学生往往离开，因为留下来不值得，而不是因为他们有可能辍学。表现不佳大大降低了学校的乐趣，并大大影响了对大学毕业后收入的信念。
We estimate a dynamic learning model of college dropout, taking advantage of unique expectations data to greatly reduce our reliance on standard assumptions. Our simulations show that 45% of dropout in the first 2 years of college can be attributed to what students learn about their academic performance, with this type of learning playing a smaller role later in college. Poorly performing students tend to leave because staying is not worthwhile rather than because they are at risk of failing out of school. Poor performance substantially decreases the enjoyability of school and substantially influences beliefs about postcollege earnings.
DOI：
10.3386/w15087
发表时间：
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期刊：
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影响因子：
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作者：
Attanasio O
通讯作者：
Attanasio O
影响因子：
6.3
作者：
van der Klaauw W;Wolpin KI
通讯作者：
Wolpin KI